The objective of the article is to indicate a tool that allows finding the relationship between cutting forces and surface roughness and variable cutting parameters and cutting tool wear. The tests were carried out on a workpiece of Inconel 718, machined with cemented carbide milling cutters. The article compares artificial neural networks and multiple regression. Multi-layer Perceptron networks with backward error propagation were used. Based on the conducted research, it was found that in the case of Inconel 718 machining, multiple regression is not a suitable tool for testing the relation analyzed in this article. The correlation (R2) for multiple regression is about 0.6 for forces and 0.25 for roughness. Neural networks have a correlation coefficient (R2) higher than 0,9.


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    Title :

    Surface roughness and forces prediction of milling Inconel 718 with neural network




    Publication date :

    2020-06-01


    Size :

    1141459 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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